J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2025 , Vol. 30 >Issue 6: 1188 - 1194

DOI: https://doi.org/10.1007/s12204-023-2669-9

Automation & Computer Technologies

Lifespan Prediction of Electronic Card in Nuclear Power Plant Based on Few Samples

Expand
  • 1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Fujian Fuqing Nuclear Power Co., Ltd., Fuqing 350318, Fujian, China; 3. Key Laboratory of System Control and Information Processing of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2023-06-16

  Accepted date: 2023-07-08

  Online published: 2023-11-06

Fold

Abstract

A lifespan prediction model was developed based on a few samples to provide decision-making information for operation and maintenance, as well as improve the economy and safety of nuclear power plant (NPP) operations. This paper applies a Weibull model to forecast the lifespan of electronic cards with a few samples in NPPs. Relationship between the lifespan prediction of electronic cards and the ambient temperature is revealed using the Arrhenius equation. Censored samples are used to compensate for the lack of fault electronic card data. Scale parameter and shape parameter of the Weibull model are optimized by adjusting the weight ratio between the censored data and the fault data. Characteristic life is then obtained using the rank regression fitting equation. Parameters of the Arrhenius equation can be calculated by dividing the samples into groups according to the ambient temperature. A case study of the intermediate range high-voltage electric card of ex-core neutron detectors demonstrates that the lifespan prediction of electronic cards in NPPs can be successfully predicted with a few samples by combining the Weibull model and the Arrhenius model. This can help provide preventive maintenance recommendations for electronic cards. Finally, operation suggestions for the electronic card’s ambient temperature can be made by utilizing the temperature-life model.

Cite this article

XU Yong, CAI Yunze, SONG Lin . Lifespan Prediction of Electronic Card in Nuclear Power Plant Based on Few Samples[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(6) : 1188 -1194 . DOI: 10.1007/s12204-023-2669-9

References

[1] XU Y, CAI Y Z, SONG L. Review of research on condition assessment of nuclear power plant equipment based on data-driven [J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 267-278 (in Chinese).

[2] YU W K, HARRIS T A. A new stress-based fatigue life model for ball bearings [J]. Tribology Transactions, 2001, 44(1): 11-18.

[3] MADAR E, KLEIN R, BORTMAN J. Contribution of dynamic modeling to prognostics of rotating machinery [J]. Mechanical Systems and Signal Processing, 2019, 123: 496-512.

[4] REN S H, XUE F, YU W W, et al. Reliability residual-life prediction method for thermal aging based on performance degradation [J]. Nuclear Power Engineering, 2013, 34(5): 96-99 (in Chinese).

[5] UTAH M N, JUNG J C. Fault state detection and remaining useful life prediction in AC powered solenoid operated valves based on traditional machine learning and deep neural networks [J]. Nuclear Engineering and Technology, 2020, 52(9): 1998-2008.

[6] LEE B S, CHUNG H S, KIM K T, et al. Remaining life prediction methods using operating data and knowledge on mechanisms [J]. Nuclear Engineering and Design, 1999, 191(2): 157-165.

[7] NGUYEN H P, FAURIAT W, ZIO E, et al. A data-driven approach for predicting the remaining useful life of steam generators [C]//2018 3rd International Conference on System Reliability and Safety. Barcelona: IEEE, 2018: 255-260.

[8] AIZPURUA J I, MCARTHUR S D J, STEWART B G, et al. Adaptive power transformer lifetime predictions through machine learning and uncertainty modeling in nuclear power plants [J]. IEEE Transactions on Industrial Electronics, 2019, 66(6): 4726-4737.

[9] WANG H, PENG M J, XU R Y, et al. Remaining useful life prediction based on improved temporal convolutional network for nuclear power plant valves [J]. Frontiers in Energy Research, 2020, 8: 584463.

[10] WANG H, PENG M J, LIU Y K, et al. Remaining useful life prediction techniques of electric valves for nuclear power plants with convolution kernel and LSTM [J]. Science and Technology of Nuclear Installations, 2020, 2020: 1-13.

[11] SAARELA O, HULSUND J E, TAIPALE A, et al. Remaining Useful Life Estimation for Air Filters at a Nuclear Power Plant[C]//2nd European Conference of the Prognostics and Health Management Society 2014. Nantes: PHM Society, 2014: 75-81.

[12] YOU D Z, LIU H, ZHANG Y P. Optimization design and reliability statistical analysis of mechanical accelerated life test based on Weibull distribution [J]. Journal of Mechanical & Electrical Engineering, 2023, 40(5): 664-672 (in Chinese).

[13] LU J, GONG P H, YE J P, et al. Learning from very few samples: A survey [DB/OL]. (2020-09-06). https://arxiv.org/abs/2009.02653

[14] HALLINAN A J Jr. A review of the weibull distribution [J]. Journal of Quality Technology, 1993, 25(2): 85-93.

[15] SHI J, SHEN D H, SHI H N, et al. Reliability data process and analysis of instrument and control switch in nuclear power plants [J]. Nuclear Power Engineering, 2010, 31(2): 54-57 (in Chinese).

[16] KANG M, LEE S, PARK H, et al. Lifetime estimation for optocouplers using accelerated degradation test [J]. Quality and Reliability Engineering International, 2022, 38(1): 560-573.

Options
Outlines

/

〈 〉